Sea Spectral Estimation Using ARMA Models [PDF]
Sensors, 2021 This paper deals with the spectral estimation of sea wave elevation time series by means of ARMA models. To start, the procedure to estimate the ARMA coefficients, based on the use of the Prony’s method applied to the auto-covariance series, is presented.
Marta Berardengo +2 more
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Regime-Switching Discrete ARMA Models for Categorical Time Series [PDF]
Entropy, 2020 For the modeling of categorical time series, both nominal or ordinal time series, an extension of the basic discrete autoregressive moving-average (ARMA) models is proposed.
Christian H. Weiß
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Revisiting inference for ARMA models: Improved fits and superior confidence intervals. [PDF]
PLoS ONEAutoregressive moving average (ARMA) models are widely used for analyzing time series data. However, standard likelihood-based inference methodology for ARMA models has avoidable limitations.
Jesse Wheeler, Edward L Ionides
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ARMA Models for Mortality Forecast
Lithuanian Journal of Statistics, 2016 In the last several decades, many countries have been paying a lot of attention to mortality forecastingbecause of high longevity risk. The purpose of this paper is to analyze mortality characteristics of Baltic countries andmake predictions using ARMA ...
Natalja Šiškina, Jonas Šiaulys
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Hybrid models combining trend and seasonality components with machine learning algorithms provide accurate forecasting of malaria incidence. [PDF]
PLOS Global Public HealthForecasting malaria incidence is vital for effective resource allocation during malaria elimination. In this study, we highlight robust models for forecasting incidence using climatic and malaria data from Goa, India.
Syed Shah Areeb Hussain +8 more
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KALMAN FILTERS AND ARMA MODELS
Ratio Mathematica, 2003 The Kalman filter is the celebrated algorithm giving a recursive solution of the prediction problem for time series. After a quite general formulation of the prediction problem, the contributions of its solution by the great mathematicians Kolmogorov and
Aniello Fedullo
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Minimum Message Length in Hybrid ARMA and LSTM Model Forecasting
Entropy, 2021 Modeling and analysis of time series are important in applications including economics, engineering, environmental science and social science. Selecting the best time series model with accurate parameters in forecasting is a challenging objective for ...
Zheng Fang +3 more
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Time Series Forecasting with UCM Model ; A Comparative Study using the Tigris River Data [PDF]
المجلة العراقية للعلوم الاحصائية, 2008 In this paper,we build two basic models to forecast a flow water of the Tigris river which enters to mosul city . The first model is Unobserved Components Model which is writing braivly by UCM,the second is Autoregressive and Moving Average model which
Thafer Ramathan Muttar +1 more
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Quantifying Influences on Intragenomic Mutation Rate
G3: Genes, Genomes, Genetics, 2020 We report work to quantify the impact on the probability of human genome polymorphism both of recombination and of sequence context at different scales.
Helmut Simon, Gavin Huttley
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Comparing PAR and MPAR Models to Modeling the Monthly River Flow Rates Time Series Under the Influence of Meteorological Factors, The Case Study: Nazloochai River [PDF]
مجله مدل سازی در مهندسی, 2018 For over three decades, hydrologists were recommended multivariate models to describe and modeling complex hydrology data. While recently the multivariate models in hydrology is discussed.
Mohammad Nazeri Tahrudi
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